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Full title: Factors influencing subjective quality of life in patients with schizophrenia
and other mental disorders: a pooled analysis
Running title: Factors influencing SQOL: a pooled analysis
Authors:
Stefan Priebea, Ulrich Reininghausa, Rosemarie McCabea, Tom Burnsb, Mona Eklundc, Lars
Hanssonc, Ulrich Junghand, Thomas Kallerte, Chijs van Nieuwenhuizenf, Mirella Ruggerig,
Mike Sladeh, Duolao Wangi
Institutional affiliation:
a
Unit for Social and Community Psychiatry, Barts and the London School of Medicine,
Queen Mary University of London, London, UK
b
University Department of Psychiatry, Warneford Hospital, Oxford, UK
c
Department of Health Sciences, Lund University, Lund, Sweden
d
University Hospital of Psychiatry, Unit of Community Psychiatry, Bern, Switzerland
e
Clinic for Psychiatry, Psychosomatics and Psychotherapy, Park Hospital Leipzig, Leipzig,
Germany
f
Department of Tranzo, Tilburg University, Tilburg, The Netherlands
g
Dipartimento di Medicina e Sanita' Pubblica, Universita’di Verona, Verona, Italy
h
Institute of Psychiatry, King’s College London, London, UK
i
London School of Hygiene and Tropical Medicine, London, UK
Correspondence to:
Stefan Priebe, Unit for Social and Community Psychiatry, Barts and the London School of
Medicine, Queen Mary University of London
Postal address: Newham Centre for Mental Health, London E13 8SP, United Kingdom; email: [email protected]
Word count (Abstract): 227
Word count (Text):
3,647
Abstract
Subjective quality of life (SQOL) is an important outcome in the treatment of patients with
schizophrenia. However, there is only limited evidence on factors influencing SQOL, and
little is known about whether the same factors influence SQOL in patients with schizophrenia
and other mental disorders. This study aimed to identify factors associated with SQOL and
test whether these factors are equally important in schizophrenia and other disorders. For this
we used a pooled data set obtained from 16 studies that had used either the Lancashire Quality
of Life Profile or the Manchester Short Assessment of Quality of Life for assessing SQOL.
The sample comprised 3936 patients with schizophrenia, mood disorders, and neurotic
disorders. After controlling for confounding factors, within-subject clustering, and
heterogeneity of findings across studies in linear mixed models, patients with schizophrenia
had more favourable SQOL scores than those with mood and neurotic disorders. In all
diagnostic groups, older patients, those in employment, and those with lower symptom scores
had higher SQOL scores. Whilst the strength of the association between age and SQOL did
not differ across diagnostic groups, symptom levels were more strongly associated with
SQOL in neurotic than in mood disorders and schizophrenia. The association of employment
and SQOL was stronger in mood and neurotic disorders than in schizophrenia. The findings
may inform the use and interpretation of SQOL data for patients with schizophrenia.
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Introduction
Subjective quality of life (SQOL) has become a widely established patient-reported outcome
in schizophrenia1. Whilst there is no consensus on the precise definition of SQOL, several
scales for assessing SQOL are based on the approach of Lehman2 which considers SQOL as
the patient’s satisfaction with life in general and with a number of major life domains.
When using SQOL findings in research studies and in the evaluation of routine care, it is
important to know which factors influence scores and need to be considered in the analysis
and interpretation of data. Factors influencing SQOL in schizophrenia may be broadly
grouped into socio-demographic and clinical ones1. Socio-demographic factors that have
frequently been studied as potentially influential are age3-7, gender4, 5, 8-11, marital status4, 5, 12,
level of education3, 13, and employment status3-5, 14-16. Several studies reported significant
associations of some of these variables with SQOL3-10, 13. A recent meta-analysis17 however
did not find robust evidence that such socio-demographic characteristics influence SQOL.
There is more consistent evidence showing that higher symptom levels are associated with
less favourable SQOL4, 5, 18-28. A meta-analysis29 found weak correlations between symptom
levels and SQOL with a substantial heterogeneity across studies, whilst Vatne and Bjoerkly17
considered a meta-regression analysis inappropriate because of the heterogeneity of
methodologies used in the relevant studies.
Initially, SQOL indicators were used in psychiatry predominantly in samples with severe
mental illnesses, most of whom were diagnosed as having schizophrenia. Over time, there has
been increasing interest in examining SQOL also in other diagnostic groups30-32. Some studies
found significant differences in SQOL between patients with schizophrenia and those with
mood disorders33, others did not17, 34. When factors influencing SQOL in these groups were
explored, similar socio-demographic and clinical factors were suggested as for patients with
schizophrenia. However, the question as to whether the same factors influence SQOL in
patients with schizophrenia and in other diagnostic groups has not been systematically
investigated. Such evidence appears essential to assess whether research findings on SQOL
from one diagnostic group can be generalised to others.
This exploratory study aimed to identify factors influencing SQOL and test whether these
factors differ between patients with schizophrenia and those with other mental disorders. For
this, we conducted a pooled analysis using original data from different studies. Unlike a
conventional meta-analysis, a pooled analysis considers not only studies but also individual
patients as the unit of analysis. Such pooled analysis has several advantages as compared to
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conventional meta-analytic techniques: it enables a more precise estimate of effects of
influential factors; allows for the control of confounding factors including within-subject
clustering and heterogeneity of findings across studies; reduces the effect of the heterogeneity
arising from the aggregation of methodologically diverse studies by using the same statistical
model; and makes it possible to test main effects as well as interactions, which is crucial for
exploring whether factors have a similar influence in patients with schizophrenia and other
diagnostic groups35, 36.
Methods
In a pooled analysis linear mixed models were applied to a large set of individual patient-level
data from samples of patients with schizophrenia, mood disorders, and neurotic disorders,
using the mean SQOL score as the dependent variable.
Sample
The pooled data set was collected specifically for the purpose of the current study. Given the
heterogeneity of measurement methods used to assess SQOL identified by systematic reviews
and meta-analyses17, only data sets using either the Lancashire Quality of Life Profile
(LQOLP)37 or its short version, the Manchester Short Assessment of Quality of Life
(MANSA)38 were considered for the pooled database. To identify relevant data sets we
contacted experts in the field and conducted a literature search of academic databases.
We aimed to use clear cut diagnostic categories with sufficient sample sizes of each category
in the complex analysis. We therefore included only patients with documented diagnoses of
schizophrenia, schizotypal, or delusional disorders (F2), mood disorders (F3), or neurotic,
stress-related, and somatoform disorders (F4) according to ICD-10 (World Health
Organization, 1993). Patients with less frequent and unclear diagnoses were excluded. If
available another SQOL assessment, obtained for the same patients at a later point of time,
were included in the study to achieve more precise estimates of effects by increasing the
number of observations whilst controlling for confounding by within-subject clustering. For
studies with more than two time points the first and last one were used to have long and
similar periods of time between the two measurements.
Measures
Studies included into the pooled data set used either the LQOLP or MANSA for measuring
SQOL. The LQOLP was theoretically based on Lehman’s approach for measuring SQOL2.
LQOLP and MANSA contain items on patients’ satisfaction with life in general and different
life domains which are rated on a scale from 1 (= couldn’t be worse) to 7 (= couldn’t be
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better). They have been shown to yield practically identical SQOL scores38. Their reliability
and validity have been demonstrated in several studies38-43 and they are widely used SQOL
measures in mental health service research in Europe. Limiting the investigation to studies
using one of these two measures was intended to have consistent data and fully utilise the
advantages of a pooled analysis.
Consistent information was available for the following socio-demographic and clinical
characteristics hypothesized to be influencing SQOL: age, gender, marital status (married /
partnership, other), level of education (to school level, to further level, to higher level; the
exact definitions of each level varied to accommodate country-specific education systems)
employment status (unemployed, other), type of current treatment (inpatient, outpatient),
clinical diagnosis, and level of psychiatric symptoms. In the majority of studies (n=13),
symptoms were assessed on the Positive and Negative Syndrome Scale (PANSS)44 or Brief
Psychiatric Rating Scale (BPRS)45. This allowed for computing BPRS-18 total sum scores as
well as sum scores of five BPRS-18 subscales: anxiety/depression, anergia, thought disorder,
activity, and hostility.
Statistical Analysis
Stata 10 for Windows was used for all data analyses46. Linear mixed models were used to
identify factors associated with subjective quality of life in different diagnostic groups whilst
controlling for confounding factors, within-subject clustering of paired measurements and
heterogeneity across studies using xtmixed and gllamm in Stata 1046, 47. In this three-level
model, paired measurements (level-1) were treated as nested within patients (level-2), and
patients nested within studies (level-3). Observations at level-1 were assumed to be missing
at random. The modelling for identifying factors influencing SQOL proceeded through three
stages. (1) Mixed models were fitted with SQOL scores as dependent variable and fixed
effects adjusted for the time of measurement for the following set of independent variables:
age, gender, marital status, level of education, employment status, type of current treatment,
ICD-10 clinical diagnosis, and symptom level. (2) All fixed effects identified as statistically
significant in stage (1) were entered into a multivariate mixed model adjusted for time point,
country of residence, and time to follow-up as a priori confounders. (3) Two-way interaction
terms for significant fixed effects x diagnosis were added one by one to the multivariate
mixed model to establish whether the effects of factors influencing SQOL identified in stage 2
differed across diagnostic groups. Statistical significance of interaction terms was assessed
using likelihood ratio tests to evaluate improvement of model fit. In all stages, heterogeneity
of findings across the included study samples was controlled for using likelihood ratios to
assess whether adding a random slope for the respective independent variables significantly
5
improved the model fit. Sensitivity analyses were performed on variables for which
significant heterogeneity was identified by assessing whether statistical significance and
magnitude of fixed effects were affected when omitting one by one those studies (a) with
cross-sectional design, prospective-observational design, and RCTs, and (b) with small vs.
large sample sizes (i.e. n ≤ 100)48.
Results
Included studies and samples
Sixteen studies 30, 31, 34, 49-60 , including one unpublished study (Junghan et al. 2009,
unpublished data), were included into the pooled data set. The characteristics are shown in
Table 1. Twelve studies were observational, six each cross-sectional and longitudinal, and
four were randomized controlled trials (RCTs). Studies were from 34 sites in 17 European
countries. Sample sizes ranged from 74 to 1055 patients. Table 1 also shows the SQOL
scores of the patients in each study.
Insert Table 1 about here
From the 16 included studies, a total of 4478 patients were available for the pooled analysis.
Of these, single assessments of SQOL mean scores were available for a total of 4285 patients.
From the total sample patients with a main clinical ICD-10 diagnosis of an organic disorder
(F0; n = 37), mental and behavioural disorders due to psychoactive substance use (F1; n =
116), behavioural syndromes associated with physiological disturbances and physical factors
(F5; n = 36), disorders of adult personality and behaviour (F6; n = 145), mental retardation
(F7; n = 5), disorders of psychological development (F8; n = 5), behavioural and emotional
disorders with onset usually occurring in childhood and adolescence (F90-98; n=5), and those
with unclear diagnosis (n = 492) were excluded. Hence, single assessments of SQOL mean
scores were available for a total of 3936 patients with schizophrenia, schizotypal, or
delusional disorders (F2, n = 2393), mood disorders (F3, n = 651), or neurotic, stress-related
and somatoform disorders (F4, n = 892). Paired assessments were available from 10 studies34,
49, 52, 53, 56-60
including the unpublished study (Junghan et al. 2009, unpublished data), with n =
2196 patients having complete SQOL mean ratings at two time points. The mean duration
between the two assessments was 17.5 months (SD = 8.1, range = .89 to 44.7).
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Patient characteristics and SQOL
The mean age of patients in the pooled data set was 40.7 years (SD = 11.8), with a roughly
equal distribution of gender (female, n = 1784, 45.4%). About half of all patients were
unemployed (n = 1852, 49.1%) and educated to school level (n = 1196, 51.9%; to further
level, n = 755, 32.8%; to higher level, n = 352, 15.3%). The majority of patients were
unmarried or not living in partnership (n = 2452, 70.2%), being treated as outpatients (n =
2596, 66.0%), and had been diagnosed with schizophrenia (n = 2393, 60.8%; mood disorder,
n = 651, 16.5%; neurotic disorder, n = 892, 22.7%). Mean symptom levels were moderate
(mean = 36.5, SD = 10.5, range 18 to 91).
Factors associated with SQOL
Findings on the association of potentially influential factors and SQOL as dependent variable
controlling for time point, within-subject clustering and heterogeneity of findings across
studies are shown in Table 2.
Insert Table 2 about here
In these analyses, almost all variables were significantly associated with SQOL apart from
gender, level of education, and service setting. There was a significant heterogeneity across
studies for age, gender, diagnosis and symptom levels.
Table 3 shows multivariate mixed models analysis for potential influential factors and SQOL
as dependent variable.
Insert Table 3 about here
In this step of the analysis, age, employment status, diagnosis, and BPRS total and subscale
scores were significantly associated with SQOL. All these factors were independently
associated with SQOL. Older patients, those with employment, those with schizophrenia and
those with lower symptom levels had significantly higher SQOL scores. All BPRS subscales
were significantly associated with SQOL scores. With respect to diagnosis there was a
significant heterogeneity of findings across studies. The sensitivity analyses showed that
fixed effects for the differences between diagnostic groups remained statistically significant
when omitting studies with different study designs. Including study design as a fixed effect
into the model did not alter the statistical significance of the fixed effects for differences
between schizophrenia and mood disorders (Standardized beta -.44, Unstandardized beta -.43,
95% CI -.54 to -.33, P < .001) or schizophrenia and neurotic disorders (Standardized beta -
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.39, Unstandardized beta -.38, 95% CI -.51 to -.26, P < .001), with only minor attenuation in
the magnitude of differences. However, when the model was re-fitted omitting studies with
small sample sizes, the heterogeneity was not statistically significant anymore (χ2 = 5.15,
P=.076). Given the heterogeneity of findings on diagnosis and SQOL across studies, all
subsequent analyses were adjusted for this.
Factors influencing SQOL in patients with schizophrenia and other disorders
Interaction effects of diagnosis and factors identified as significantly associated with SQOL in
the previous multivariate mixed model are summarised in Table 4.
Insert Table 4 about here
A likelihood ratio test showed an equally strong association of age with SQOL in all
diagnostic groups (χ2 = 1.67, P=.434). However, there was a significant interaction for
diagnosis by employment status (χ2 = 10.27, P = .006). The effects of unemployment were
significantly weaker in patients with schizophrenia than in those with mood disorders (F3 vs.
F2, P < .001) and neurotic disorders (F4 vs. F2, P < .001). There was no significant
difference between patients with neurotic disorders and mood disorders (F4 vs. F3, P = .217),
and findings on the interaction of employment status by diagnosis did not vary significantly
across studies.
Symptom levels were inversely related to SQOL in patients with schizophrenia, mood
disorders, and neurotic disorders. There was however a significant interaction effect for
diagnosis by symptoms levels (χ2 = 6.78, P = .034). While higher symptom levels were
associated with lower SQOL in patients of all diagnostic groups, the influence of symptom
levels was significantly stronger in patients with neurotic disorders than in those with
schizophrenia (F4 vs. F2, P = .009) and mood disorders (F4 vs. F3, P = .044). There was no
such a difference between patients with mood disorders and schizophrenia (F3 vs. F2, P =
.714). No significant heterogeneity of findings on the interaction of diagnosis by symptom
levels was found across studies.
Discussion
Main findings
The pooled analysis found more favourable SQOL scores in patients with schizophrenia than
in groups with other diagnoses. It showed also higher SQOL scores in older patients, those
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with employment and those with lower symptom levels. However, the findings suggest that
the influence of factors other than age varies across diagnostic groups. The association of
symptoms with SQOL was significantly stronger in patients with neurotic disorders than in
those with schizophrenia and mood disorders. The influence of employment was significantly
stronger in patients with mood and neurotic disorders than in those with schizophrenia. For
only one influential factor, i.e. diagnosis, we found a significant heterogeneity of findings
across studies. The sensitivity analysis suggested that this heterogeneity was not accounted
for by the study design, but by an absence of significant differences between diagnostic
groups in studies with small sample sizes.
Strengths and limitations
This is the largest study to date using a pooled data set to analyse factors influencing SQOL in
patients with schizophrenia and other mental disorders. The analysis considered both studies
and individual patients as units of analysis. Overcoming some of the limitations of
conventional meta-analytic techniques, the study therefore complements meta-analytical
findings. Further strengths are that the sample size was sufficiently large for this type of
complex analysis (although it varied for different analyses in this study), and that for the
majority of patients the analysis included two assessments at different points of time to
increase the precision of the findings whilst controlling for within-subject clustering. SQOL
data and other characteristics were assessed with very similar methods and categories across
all included data sets, so that potential inconsistencies of assessment methods were practically
excluded as a source of heterogeneity.
The study has four major limitations. All of them are related to the nature of the pooled set
which was generated driven by the availability of data that has been collected with
sufficiently consistent methods: A) We considered only studies using the LQOLP and
MANSA. B) Not all patients included in each study were randomly selected to represent all
patients in the given service. Selection biases might have influenced the levels of SQOL
found in each diagnostic group. C) The number of factors in the analysis was limited. Further
factors of potential relevance such as length of illness or details of current treatment were not
considered. Such further factors might have explained the differences found between groups
in this study. D) Only crude categories of each variable were analysed.
Comparisons with previous research
In this analysis patients with schizophrenia had more positive SQOL scores than patients with
other diagnoses. This cannot be explained by possible differences in the considered sociodemographic characteristics or in symptom levels. The data of this study do not provide any
9
evidence for the possible reasons for this difference. One can only speculate whether patients
with schizophrenia tend to adapt to the experience of living with their disorder in different
ways than patients with other disorders2, 33. They might have lowered their expectations
resulting in a higher satisfaction with life, found more appropriate methods to reach
satisfaction in difficult circumstances, or be less prone to rate items in questionnaires based
on negative emotions. The difference may also reflect schizophrenia-specific clinical
characteristics such as emotional withdrawal or affective blunting, which may limit the
impact of disorder-related dysfunction on SQOL and lead to higher ratings in this group33.
Similar factors might also reduce the impact of symptom levels on SQOL in patients with
schizophrenia, which in this study was weaker than in patients with neurotic disorders.
Despite the specific differences between the diagnostic groups, higher symptom levels were
associated with less favourable SQOL in all groups. This general association of symptom
levels and SQOL is in line with previous research including the meta-analysis of Eack and
Newhill29. It applies to all subscales of the BPRS, suggesting that SQOL ratings are
influenced by overall levels of current illness severity. However, the subscale on depression
and anxiety shows the strongest association with SQOL. This underlines the particular
importance of such symptoms for SQOL ratings which has been repeatedly suggested in the
literature2, 22, 33. Based on our findings, an increase of BPRS total scores of 8.1 (i.e. 1 standard
deviation), corresponds to a decrease in SQOL mean scores of .23 in patients with
schizophrenia (i.e. using the standardised coefficient in Table 4 and a standard deviation of
.98), .25 in patients with mood disorders, and .42 in patients with neurotic disorders. Hence,
the association of overall symptom levels with SQOL appears clinically relevant.
When the influence of other factors was controlled for, marital status did not affect SQOL. It
may be possible that positive and negative effects of living with a partner cancel each other
out. The lack of an association of marital status and gender with SQOL is consistent with
findings of the meta-analysis of Vatne and Bjoerkly17. Younger age and unemployment were
associated with lower SQOL, also findings that are consistent with previous research. The
influence of unemployment was weaker in patients with schizophrenia than in those with
other disorders, again possibly linked to lower expectations or disorder specific cognitive and
emotional processes of appraising an adverse objective situation.
With respect to the methodology of the studies assessing SQOL, the pooled analysis reduced
the effect of the heterogeneity of findings across studies by using the same statistical model.
In the final model a significant heterogeneity was identified only for the variable diagnostic
group. The sensitivity analysis showed that this heterogeneity of findings was explained by
10
the absence of significant differences between diagnostic groups in studies with small sample
sizes. One may conclude that studies with small sample sizes tend to underestimate
differences between diagnostic groups, whilst the study design does not make difference.
Implications
The factors identified in this analysis as being associated with SQOL explain only small
amounts of variance. Whether such variance is important or not, is likely to depend on the
type of intervention to be evaluated and the purpose of a study. Small differences in SQOL as
an outcome can be very relevant in studies testing inexpensive interventions that can easily be
rolled out across services58, or in evaluations of services in large populations. In such studies,
analyses may adjust for the influence of the factors identified in this analysis. The factors may
also inform the allocation strategy in experimental studies, e.g. stratifying a random allocation
by age, employment status, diagnostic group and/or symptom level. In studies that aim to
identify large effect sizes in SQOL the associations with influential factors reported in this
analysis may be less relevant.
The analysis identified factors influencing SQOL in mostly observational designs. Based on
this analysis alone, no firm conclusions can be drawn about possible interventions for
improving SQOL. Some evidence from the literature suggests that symptom improvement and
employment schemes can lead to improvements of SQOL61, 62. One might hypothesize that
such an effect may be smaller in patients with schizophrenia than in those with neurotic
disorders, reflecting the difference in the strength of the influence of symptoms and
employment status on SQOL between diagnostic groups found in this study. In any case the
study can be used to estimate the gains in SQOL that may be expected in case of defined
improvements of symptom levels. Moreover, the findings suggest that such estimates should
be specific for different diagnostic groups and that not all findings of SQOL research can be
generalised from one diagnostic group to others.
Considering the more favourable SQOL in patients with schizophrenia as compared to other
diagnostic groups, one may conclude that further improvements through therapeutic
interventions are more difficult to achieve, and that a ceiling effect may be more easily
reached. This should be considered in the evaluation of routine care when SQOL is used as an
outcome criterion. Research studies evaluating interventions to improve SQOL in patients
with schizophrenia might focus on patients with lower SQOL baseline scores. Including all
those patients who already have a relatively positive SQOL may make it more difficult to
detect significant effects of a tested intervention.
11
More specific research including qualitative studies may explore why patients with
schizophrenia have more positive SQOL than other diagnostic groups, and why their ratings
are less influenced by symptoms and employment status.
Funding
This work was supported by a Research Training Fellowship funded by the National Institute
for Health Research (UK) to U.R. The report is independent research and the views
expressed in this publication are those of the authors and not necessarily those of the NHS,
the National Institute for Health Research or the Department of Health.
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17
Tables
Table 1. Study characteristics of the 16 included studies
Study
Study sites
Roeder-Wanner & Priebe59
Research design
SQOL
Measure1
LQOLP
SQOL
(baseline)
mean (SD)
4.52 (.75)
LQOLP
4.25 (.74)
676
417
Cross-sectional
LQOLP
4.69 (.75)
Eklund et al.50
Priebe et al.57
London (UK)
Manchester (UK)
Malmoe (Sweden)
Landskrona (Sweden)
Umea (Sweden)
Copenhagen (Denmark)
Roskilde (Denmark)
Arhus (Denmark)
Turku (Finland)
Bodo (Norway)
Reykjavik (Iceland)
Malmoe (Sweden)
Berlin (Germany)
Prospectiveobservational
RCT
74
100
LQOLP
LQOLP
4.23 (.72)
4.65 (.79)
Ruggeri et al.34
Verona (Italy)
389
LQOLP
4.57 (.90)
Zagreb (Croatia)
Belgrade (Serbia)
Sarajevo (Bosnia)
Rijeka (Bosnia)
London (UK)
London (UK)
London (UK)
London (UK)
London (UK)
Granada (Spain)
Groningen (Netherlands)
Lund (Sweden)
Mannheim (Germany)
Zuerich (Switzerland)
Umea (Sweden)
Stockholm (Sweden)
Falkenberg (Sweden)
Boras (Sweden)
Eskilstuna (Sweden)
Sollentuna (Sweden)
Osteraker (Sweden)
London (UK)
Dresden (Germany)
London (UK)
Wroclaw (Poland)
Michalovce (Slovakia)
Prague (Czech Republic)
Bern (Switzerland)
493
Cross-sectional
Prospectiveobservational
Prospectiveobservational
Prospectiveobservational
MANSA
3.44 (.81)
Burns et al.49
Hansson et al.51
Priebe et al.56
McCabe & Priebe54
d’Ardenne et al.30
Rakib et al.31
Slade et al.60
Priebe et al.58
Hansson et al.52
McGuire-Snieckus et al.55
Kallert et al.53
Junghan (unpublished data)
1
Berlin (Germany)
Sample
size
n
89
124
110
69
133
447
Cross-sectional
Cross-sectional
Cross-sectional
RCT
RCT
MANSA
MANSA
MANSA
MANSA
MANSA
4.43 (.85)
3.13 (.85)
4.11 (.69)
4.25 (1.00)
4.70 (.88)
129
Prospectiveobservational
LQOLP
4.33 (.77)
111
963
Cross-sectional
RCT
MANSA
MANSA
5.12 (1.39)
3.96 (.92)
133
Prospectiveobservational
LQOLP
4.72 (1.11)
LQOLP, Lancashire Quality of Life Profile; MANSA, Manchester Short Assessment of Quality of Life
18
Table 2. Univariate mixed models analysis of potential influential factors and SQOL as dependent variable a
Fixed part
Random partb
Total
Study-level
Level-1
Influential factor
n
Standardized
Unstandardized Beta
studies
P
random intercept
residual
Beta
(95% CI)
variance
variance
Age
Female vs. male
Level of education
To school vs. further level
To school vs. higher level
Married/partnership vs. other
Unemployed vs. other
Outpatient vs. inpatient care
15
16
10
3806
3928
2303
12
15
16
3493
3771
3936
ICD-10 clinical diagnosis
Mood disorders vs.
Schizophrenia
Neurotic disorders vs.
Schizophrenia
BPRS-18 total score
Depression / anxiety
subscale
Anergia subscale
Thought disorder subscale
Activation subscale
Hostility subscale
16
3936
.05
.02
.004 (.0002 to .007)
.02 (-.06 to .10)
.04
.03
.12
-.33
.05
.04 (-.04 to .12)
.03 (-.08 to .13)
.12 (.05 to .18)
-.32 (-.38 to -.25)
.05 (-.07 to .17)
.036
.587
.606
.322
.611
<.001
<.001
.441
.195
.223
.060
.331
.334
.353
.149
.278
.236
.332
.334
.334
<.001
.189
.336
Study-level
random slope
variancec
Heterogeneityc
.001
.011
-
χ2 = 12.91, P = .002
χ2 = 6.73, P = .035
χ2 = .96, P = .619
-
χ2 = 2.17, P = .338
χ2 = .14, P = .935
χ2 = .86, P = .650
χ2 = 45.37, P <.001
-.35
-.34 (-.42 to -.26)
<.001
.296
-.82
-.80 (-.87 to -.73)
<.001
.178
11
2397
-.30
-.03 (-.04 to -.02)
<.001
.068
.358
.001
χ2 = 24.83, P < .001
11
2397
-.36
-.09 (-.10 to -.07)
<.001
.032
.358
.001
χ2 = 12.16, P =.002
11
11
2397
2397
11
2397
-.09
-.12
-.05
-.25
-.024 (-.049 to .001)
-.03 (-.04 to -.02)
-.02 (-.04 to -.01)
-.10 (-.14 to -.07)
.058
<.001
.004
<.001
.095
.089
.281
.073
.361
.361
.365
.359
.002
.001
.005
χ2 = 10.53, P =.005
χ2 = 6.96, P = .031
χ2 = 1.87, P = .392
χ2 = 22.07, P < .001
a
Estimates are adjusted for time point
Patient-level random-intercept variance was of trivial magnitude and is therefore not shown here
Heterogeneity of findings across studies was tested using likelihood ratio tests to assess whether inclusion of random slopes improved model fit. Random slopes were not included into the model when likelihood ratio
tests were not significant.
b
c
19
Table 3. Multivariate mixed models analysis of potential influential factors and SQOL as dependent variable (8 studies, 2063 patients) a
Fixed part
Random partb
Study-level
Level-1
Study-level
Influential factor
Standardized
Unstandardized beta
P
random intercept
residual
random slope
beta
(95% CI)
variance
variance
variancec
.160
.356
Age
.075
.006 (.003 to .009)
<.001
-
χ2 = .75, P = .194
Married/partnership vs. other
.06
.06 (-.03 to .14)
.173
-
χ2 = 0.01, P = .999
Unemployed vs. other
ICD-10 clinical diagnosis
Mood disorders vs. Schizophrenia
Neurotic disorders vs. Schizophrenia
BPRS-18 total score
Depression / anxiety subscalec
Anergia subscale
Thought disorder subscale
Activation subscale
Hostility subscale
-.17
-.17 (-.25 to -.09)
-
χ2 = .01 P = 1.00
-.49
-.43
-.250
-.357
-.059
-.101
-.051
-.194
-.47 (-.58 to -.37)
-.42 (-.54 to -.29)
-.024 (-.027 to -.020)
-.079 (-.088 to -.070)
-.017 (-.028 to -.006)
-.026 (-.037 to -.016)
-.022 (-.038 to -.006)
-.074 (-.087 to -.060)
<.001
<.001
<.001
<.001
<.001
<.001
<.001
<.001
.007
<.001
.130
.049
.002
-
χ2 = 7.30, P = .026d
.131
.0001
.198
.201
.169
.356
.197
.358
.358
.356
Heterogeneityc
χ2 = .72, P = .198
χ2 = 5.14, P = .012e
χ2 = .01, P = .460
χ2 = 2.55, P = .055
χ2 = -99.92, P = 1.00
χ2 = .44, P = .255
a
Estimates are adjusted for time point, country of residence, and time to follow-up
Patient-level random-intercept variance was of trivial magnitude and is therefore not shown here
c
Heterogeneity of findings across studies was tested using likelihood ratio tests to assess whether inclusion of random slopes improved model fit. Random slopes were not included into the model when likelihood ratio
tests were not significant.
d
Sensitivity analysis for fixed effects for ICD-10 clinical diagnosis:
Study design: Omission of cross-sectional studies (P<.001), prospective-observational studies (P<.001), RCTs (P<.001)
Sample size:
Omission of studies with n≤100 (P<.001), n>100 (P=.392)
e
Sensitivity analysis for fixed effects of BPRS depression / anxiety subscale:
Study design: Omission of cross-sectional studies (P<.001), prospective-observational studies (P<.001), RCTs (P<.001)
Sample size:
Omission of studies with n≤100 (P<.001), n>100 (P<.001)
b
20
Table 4. Multivariate mixed models analysis of factors influencing SQOL in schizophrenia, mood disorders, and neurotic disorders (8 studies, 2063 patients)a
Random partb
Fixed part
Influential factor
Standardized
beta
Unstandardized beta
(95% CI)
P
ICD-10 clinical diagnosis x employment status
Study-level
random intercept
variance
Level-1
residual
variance
.177
.357
Study-level
random slope
variancec
Heterogeneityc
Unemployed vs. other
Schizophrenia
-.105
-.102 (-.192 to -.012)
.026
-
Mood disorders
-.823
-.802 (-.978 to -.626)
<.001
-
Neurotic disorders
-.659
-.642 (-.852 to -.431)
<.001
-
ICD-10 clinical diagnosis x BPRS-18 total score
.176
χ2 = 0.01,
P = .999
.356
Schizophrenia
-.237
-.022 (-.026 to -.019)
<.001
-
Mood disorders
-.257
-.024 (-.034 to -.015)
<.001
-
Neurotic disorders
-.429
-.041 (-.054 to -.027)
<.001
-
χ2 = .01,
P = .469
a
Interaction terms were added to the previous adjusted multivariate mixed model using a likelihood ratio test for assessing their statistical significance
Patient-level random-intercept variance was of trivial magnitude and is therefore not shown here
Heterogeneity of findings on interaction terms across studies was tested using likelihood ratio tests to assess whether inclusion of random slopes improved model fit. Random slopes were not included into the model
when likelihood ratio tests were not significant. A random slope for diagnosis was always entered into the model to control for the previously identified heterogeneity of findings across studies for this variable
b
c
21